13.3.12.14 Graph Embedding Clustering

Yan, S.C.[Shui-Cheng], Xu, D.[Dong], Zhang, B.Y.[Ben-Yu], Zhang, H.J.[Hong-Jiang], Yang, Q.A.[Qi-Ang], Lin, S.,
Graph Embedding and Extensions: A General Framework for Dimensionality Reduction,
PAMI(29), No. 1, January 2007, pp. 40-51.
IEEE DOI 0701
Graph embedding formulation to unify various dimensionality reduction techniques. An intrinsic graph and a penalty graph to implement Marginal Fisher Analysis. Overcome limitations of LDA. BibRef

Yan, S.C.[Shui-Cheng], Xu, D.[Dong], Zhang, B.Y.[Ben-Yu], Zhang, H.J.[Hong-Jiang],
Graph Embedding: A General Framework for Dimensionality Reduction,
CVPR05(II: 830-837).
IEEE DOI 0507
BibRef

Han, L.[Lin], Escolano, F.[Francisco], Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
Graph characterizations from von Neumann entropy,
PRL(33), No. 15, 1 November 2012, pp. 1958-1967.
Elsevier DOI 1210
BibRef
Earlier: A3, A1, A4, Only:
Information theoretic methods for learning generative models for relational structures,
SIG11(692-693).
IEEE DOI 1201
BibRef
Earlier: A2, A3, Only:
The Mutual Information between Graphs,
ICPR14(94-99)
IEEE DOI 1412
Graph characterizations; Von Neumann entropy; Estrada's heterogeneity index; Thermodynamic depth complexity. Entropy BibRef

Han, L.[Lin], Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
Characterizing Graphs Using Approximate von Neumann Entropy,
IbPRIA11(484-491).
Springer DOI 1106
BibRef
And:
Entropy versus Heterogeneity for Graphs,
GbRPR11(32-41).
Springer DOI 1105
BibRef
And:
Learning Generative Graph Prototypes Using Simplified von Neumann Entropy,
GbRPR11(42-51).
Springer DOI 1105
BibRef
Earlier: A1, A3, A2:
A Supergraph-based Generative Model,
ICPR10(1566-1569).
IEEE DOI 1008
From supergraph via edit operations. BibRef

Ye, C.[Cheng], Wilson, R.C.[Richard C.], Hancock, E.R.[Edwin R.],
A Jensen-Shannon Divergence Kernel for Directed Graphs,
SSSPR16(196-206).
Springer DOI 1611
BibRef

Bai, L.[Lu], Hancock, E.R.[Edwin R.],
Graph Kernels from the Jensen-Shannon Divergence,
JMIV(47), No. 1-2, September 2013, pp. 60-69.
WWW Link. 1307
BibRef
And:
A Fast Jensen-Shannon Subgraph Kernel,
CIAP13(I:181-190).
Springer DOI 1311
BibRef
Earlier:
Graph Complexity from the Jensen-Shannon Divergence,
SSSPR12(79-88).
Springer DOI 1211
BibRef
Earlier:
Graph Clustering Using the Jensen-Shannon Kernel,
CAIP11(I: 394-401).
Springer DOI 1109
BibRef

Bai, L.[Lu], Bunke, H.[Horst], Hancock, E.R.[Edwin R.],
An Attributed Graph Kernel from the Jensen-Shannon Divergence,
ICPR14(88-93)
IEEE DOI 1412
Accuracy BibRef

Bai, L.[Lu], Hancock, E.R.[Edwin R.], Han, L.[Lin],
A Graph Embedding Method Using the Jensen-Shannon Divergence,
CAIP13(102-109).
Springer DOI 1308
BibRef

Bai, L.[Lu], Rossi, L.[Luca], Cui, L.X.[Li-Xin], Zhang, Z.H.[Zhi-Hong], Ren, P.[Peng], Bai, X.[Xiao], Hancock, E.R.[Edwin R.],
Quantum kernels for unattributed graphs using discrete-time quantum walks,
PRL(87), No. 1, 2017, pp. 96-103.
Elsevier DOI 1703
BibRef
Earlier: A1, A4, A5, A2, A7, Only
An Edge-Based Matching Kernel Through Discrete-Time Quantum Walks,
CIAP15(I:27-38).
Springer DOI 1511
BibRef
And: A1, A2, A5, A4, A7, Only:
A Quantum Jensen-Shannon Graph Kernel Using Discrete-Time Quantum Walks,
GbRPR15(252-261).
Springer DOI 1511
Graph kernels BibRef

Bai, L.[Lu], Rossi, L.[Luca], Torsello, A.[Andrea], Hancock, E.R.[Edwin R.],
A quantum Jensen-Shannon graph kernel for unattributed graphs,
PR(48), No. 2, 2015, pp. 344-355.
Elsevier DOI 1411
BibRef
Earlier: A2, A3, A4, Only:
Manifold Learning and the Quantum Jensen-Shannon Divergence Kernel,
CAIP13(62-69).
Springer DOI 1308
Graph kernels
See also Clustering and Embedding Using Commute Times. BibRef

Bai, L.[Lu], Ren, P.[Peng], Hancock, E.R.[Edwin R.],
A Hypergraph Kernel from Isomorphism Tests,
ICPR14(3880-3885)
IEEE DOI 1412
Accuracy BibRef
Earlier: A1, A3, A2:
A Jensen-Shannon Kernel for Hypergraphs,
SSSPR12(181-189).
Springer DOI 1211
BibRef
And: A1, A3, A2:
Jensen-Shannon graph kernel using information functionals,
ICPR12(2877-2880).
WWW Link. 1302
BibRef

Wu, M.H.[Mei-Hong], Zeng, Y.B.[Yang-Bin], Zhang, Z.H.[Zhi-Hong], Hong, H.Y.[Hai-Yun], Xu, Z.B.[Zhuo-Bin], Cui, L.X.[Li-Xin], Bai, L.[Lu], Hancock, E.R.[Edwin R.],
Directed Network Analysis Using Transfer Entropy Component Analysis,
SSSPR18(491-500).
Springer DOI 1810
BibRef

Cui, L.X.[Li-Xin], Bai, L.[Lu], Rossi, L.[Luca], Zhang, Z.H.[Zhi-Hong], Xu, L.X.[Li-Xiang], Hancock, E.R.[Edwin R.],
A Mixed Entropy Local-Global Reproducing Kernel for Attributed Graphs,
SSSPR18(501-511).
Springer DOI 1810
BibRef

Bai, L.[Lu], Zhang, Z.H.[Zhi-Hong], Wang, C.Y.[Chao-Yan], Hancock, E.R.[Edwin R.],
An Edge-Based Matching Kernel for Graphs Through the Directed Line Graphs,
CAIP15(II:85-95).
Springer DOI 1511
BibRef

Bai, L.[Lu], Rossi, L.[Luca], Cui, L.X.[Li-Xin], Hancock, E.R.,
A transitive aligned Weisfeiler-Lehman subtree kernel,
ICPR16(396-401)
IEEE DOI 1705
Convolution, Entropy, Indexes, Kernel, Reliability, Standards, Steady-state BibRef

Bai, L.[Lu], Rossi, L.[Luca], Cui, L.X.[Li-Xin], Hancock, E.R.,
A novel entropy-based graph signature from the average mixing matrix,
ICPR16(1339-1344)
IEEE DOI 1705
Eigenvalues and eigenfunctions, Entropy, Kernel, Laplace equations, Probability distribution, Quantum, computing BibRef

Bai, L.[Lu], Cui, L.X.[Li-Xin], Wang, Y.[Yue], Jin, X.[Xin], Bai, X.[Xiao], Hancock, E.R.,
Shape classification with a vertex clustering graph kernel,
ICPR16(2634-2639)
IEEE DOI 1705
Digital images, Kernel, Shape, Time complexity, Videos BibRef

Zhang, Z.H.[Zhi-Hong], Ren, P.[Peng], Hancock, E.R.[Edwin R.],
Unsupervised Feature Selection Via Hypergraph Embedding,
BMVC12(39).
DOI Link 1301
BibRef

Bai, L.[Lu], Hancock, E.R.[Edwin R.], Han, L.[Lin], Ren, P.[Peng],
Graph clustering using graph entropy complexity traces,
ICPR12(2881-2884).
WWW Link. 1302
BibRef

Bai, L.[Lu], Ren, P.[Peng], Bai, X.[Xiao], Hancock, E.R.[Edwin R.],
A Graph Kernel from the Depth-Based Representation,
SSSPR14(1-11).
Springer DOI 1408
BibRef

Bai, L.[Lu], Hancock, E.R.[Edwin R.],
Fast depth-based subgraph kernels for unattributed graphs,
PR(50), No. 1, 2016, pp. 233-245.
Elsevier DOI 1512
Depth-based representations BibRef

Qiu, H.J.[Huai-Jun], Hancock, E.R.[Edwin R.],
Graph simplification and matching using commute times,
PR(40), No. 10, October 2007, pp. 2874-2889.
Elsevier DOI 0707
BibRef
Earlier:
Spanning Trees from the Commute Times of Random Walks on Graphs,
ICIAR06(II: 375-385).
Springer DOI 0610
BibRef
And:
Graph Embedding Using Commute Time,
SSPR06(441-449).
Springer DOI 0608
BibRef
And:
Graph Matching using Commute Time Spanning Trees,
ICPR06(III: 1224-1227).
IEEE DOI 0609
BibRef
And: ICPR06(IV: 955).
IEEE DOI 0609
BibRef
And:
Robust Multi-body Motion Tracking Using Commute Time Clustering,
ECCV06(I: 160-173).
Springer DOI 0608
Graph-matching; Graph simplification; Commute time; Graph spectrum BibRef

Bai, L.[Lu], Cui, L.X.[Li-Xin], Escolano, F., Hancock, E.R.[Edwin R.],
An Edge-Based Matching Kernel on Commute-Time Spanning Trees,
ICPR16(2103-2108)
IEEE DOI 1705
Computational complexity, Convolution, Hafnium, Kernel, Pattern matching, Standards BibRef

Qiu, H.J.[Huai-Jun], Hancock, E.R.[Edwin R.],
Clustering and Embedding Using Commute Times,
PAMI(29), No. 11, November 2007, pp. 1873-1890.
IEEE DOI 0711
BibRef
Earlier:
Commute Times, Discrete Green's Functions and Graph Matching,
CIAP05(454-462).
Springer DOI 0509
BibRef
And:
Commute Times for Graph Spectral Clustering,
CAIP05(128).
Springer DOI 0509

See also quantum Jensen-Shannon graph kernel for unattributed graphs, A. BibRef

Robles-Kelly, A.[Antonio], Hancock, E.R.[Edwin R.],
A Riemannian approach to graph embedding,
PR(40), No. 3, March 2007, pp. 1042-1056.
Elsevier DOI 0611
Graph embedding; Riemannian geometry; Combinatorial Laplacian BibRef

Robles-Kelly, A.[Antonio], Hancock, E.R.[Edwin R.],
Graph Matching using Adjacency Matrix Markov Chains,
BMVC01(Session 5: Matching & Retrieval).
HTML Version. University of York 0110
BibRef

Torsello, A.[Andrea], Hancock, E.R.[Edwin R.],
Graph embedding using tree edit-union,
PR(40), No. 5, May 2007, pp. 1393-1405.
Elsevier DOI 0702
2D shape; Skeleton; Tree-union; Embedding
See also Discovering Shape Classes using Tree Edit-Distance and Pairwise Clustering. BibRef

Torsello, A.[Andrea],
An importance sampling approach to learning structural representations of shape,
CVPR08(1-7).
IEEE DOI 0806
BibRef

Xiao, B.[Bai], Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
A generative model for graph matching and embedding,
CVIU(113), No. 7, July 2009, pp. 777-789.
Elsevier DOI 0905
BibRef
And: A1, A3, A2:
Quantitative Evaluation on Heat Kernel Permutation Invariants,
SSPR08(217-226).
Springer DOI 0812
BibRef
Earlier: A1, A3, A2:
Object recognition using graph spectral invariants,
ICPR08(1-4).
IEEE DOI 0812
BibRef
And: A2, A3, A1:
Characterising Graphs using the Heat Kernel,
BMVC05(xx-yy).
HTML Version. 0509
BibRef
Earlier: A2, A3, A1:
Graph Clustering using Symmetric Polynomials and Local Linear Embedding,
BMVC03(xx-yy).
HTML Version. 0409
Graph embedding; Shape analysis; Generative model; Heat-kernel analysis BibRef

Xiao, B.[Bai], Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
Graph characteristics from the heat kernel trace,
PR(42), No. 11, November 2009, pp. 2589-2606.
Elsevier DOI 0907
Heat kernel trace; Graph invariants; Image clustering and recognition BibRef

Xiao, B.[Bai], Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
Geometric characterization and clustering of graphs using heat kernel embeddings,
IVC(28), No. 6, June 2010, pp. 1003-1021.
Elsevier DOI 1003
Graph spectra; Kernel methods; Graph embedding; Differential geometry; Graph clustering BibRef

Xiao, B.[Bai], Hancock, E.R.[Edwin R.],
A Spectral Generative Model for Graph Structure,
SSPR06(173-181).
Springer DOI 0608
BibRef
Earlier:
Geometric Characterisation of Graphs,
CIAP05(471-478).
Springer DOI 0509
BibRef

Xiao, B.[Bai], Hancock, E.R.[Edwin R.],
Clustering Shapes Using Heat Content Invariants,
ICIP05(I: 1169-1172).
IEEE DOI 0512
BibRef
Earlier:
Graph Clustering Using Heat Content Invariants,
IbPRIA05(II:123).
Springer DOI 0509
BibRef

Xiao, B.[Bai], Hancock, E.R.[Edwin R.],
Trace Formula Analysis of Graphs,
SSPR06(306-313).
Springer DOI 0608
BibRef

Xiao, B.[Bai], Yu, H.[Hang], Hancock, E.R.[Edwin R.],
Graph Matching Using Manifold Embedding,
ICIAR04(I: 352-359).
Springer DOI 0409
BibRef
And:
Graph matching using spectral embedding and alignment,
ICPR04(III: 398-401).
IEEE DOI 0409
BibRef
And:
Graph Matching using Spectral Embedding and Semidefinite Programming,
BMVC04(xx-yy).
HTML Version. 0508
BibRef

Luo, B.[Bin], Wilson, R.C., Hancock, E.R.,
Graph manifolds from spectral polynomials,
ICPR04(III: 402-405).
IEEE DOI 0409
BibRef

Zhao, H.F.[Hai-Feng], Robles-Kelly, A.[Antonio], Zhou, J.[Jun], Lu, J.F.[Jian-Feng], Yang, J.Y.[Jing-Yu],
Graph attribute embedding via Riemannian submersion learning,
CVIU(115), No. 7, July 2011, pp. 962-975.
Elsevier DOI 1106
Graph embedding; Riemannian geometry; Relational matching BibRef

Zhao, H.F.[Hai-Feng], Robles-Kelly, A., Zhou, J.[Jun],
On the Use of the Chi-Squared Distance for the Structured Learning of Graph Embeddings,
DICTA11(422-428).
IEEE DOI 1205
BibRef

Czech, W.W.[Wojciech W.],
Invariants of distance k-graphs for graph embedding,
PRL(33), No. 15, 1 November 2012, pp. 1968-1979.
Elsevier DOI 1210
BibRef
Earlier:
Graph Descriptors from B-Matrix Representation,
GbRPR11(12-21).
Springer DOI 1105
Based on distances between graph vertices. Graph embedding; Graph invariants; B-matrix BibRef

Liu, X., Yan, S., Jin, H.,
Projective Nonnegative Graph Embedding,
IP(19), No. 5, May 2010, pp. 1126-1137.
IEEE DOI 1004
BibRef

Jouili, S.[Salim], Tabbone, S.A.[Salvatore A.],
Hypergraph-based image retrieval for graph-based representation,
PR(45), No. 11, November 2012, pp. 4054-4068.
Elsevier DOI 1206
BibRef
Earlier:
Towards Performance Evaluation of Graph-Based Representation,
GbRPR11(72-81).
Springer DOI 1105
BibRef
Earlier:
Graph Embedding Using Constant Shift Embedding,
ICPR-Contests10(83-92).
Springer DOI 1008
Graph indexing; Graph retrieval; CBIR BibRef

Jouili, S.[Salim], Tabbone, S.A.[Salvatore A.],
Graph Matching Based on Node Signatures,
GbRPR09(154-163).
Springer DOI 0905
BibRef

Jouili, S.[Salim], Tabbone, S.A.[Salvatore A.], Lacroix, V.[Vinciane],
Median Graph Shift: A New Clustering Algorithm for Graph Domain,
ICPR10(950-953).
IEEE DOI 1008
BibRef

Jouili, S.[Salim], Mili, I.[Ines], Tabbone, S.A.[Salvatore A.],
Attributed Graph Matching Using Local Descriptions,
ACIVS09(89-99).
Springer DOI 0909
BibRef

Fu, Y.[Yun], Ma, Y.Q.[Yun-Qian], (Eds.)
Graph Embedding for Pattern Analysis,
Springer2013. ISBN: 978-1-4614-4456-5

Hancock, E.R.[Edwin R.], Wilson, R.C.[Richard C.],
Pattern analysis with graphs: Parallel work at Bern and York,
PRL(33), No. 7, 1 May 2012, pp. 833-841.
Elsevier DOI 1203
Award, King Sun Fu, Related. An invited related paper. Graph matching; Edit distance; Graph clustering; Graph embedding BibRef

Sun, C.[Chao], Bao, B.K.[Bing-Kun], Xu, C.S.[Chang-Sheng],
Inductive hierarchical nonnegative graph embedding for 'verb-object' image classification,
MVA(25), No. 7, October 2014, pp. 1647-1659.
Springer DOI 1410
Objects and relations. BibRef

Zhang, H.W.[Han-Wang], Zha, Z.J.[Zheng-Jun], Yang, Y., Yan, S.C.[Shui-Cheng], Chua, T.S.[Tat-Seng],
Robust (Semi) Nonnegative Graph Embedding,
IP(23), No. 7, July 2014, pp. 2996-3012.
IEEE DOI 1407
Image reconstruction BibRef

Zhang, H.W.[Han-Wang], Zha, Z.J.[Zheng-Jun], Yan, S.C.[Shui-Cheng], Wang, M.[Meng], Chua, T.S.[Tat-Seng],
Robust Non-negative Graph Embedding: Towards noisy data, unreliable graphs, and noisy labels,
CVPR12(2464-2471).
IEEE DOI 1208
BibRef

Shi, X., Guo, Z., Lai, Z., Yang, Y., Bao, Z., Zhang, D.,
A Framework of Joint Graph Embedding and Sparse Regression for Dimensionality Reduction,
IP(24), No. 4, April 2015, pp. 1341-1355.
IEEE DOI 1503
Algorithm design and analysis BibRef

Xue, Z.H.[Zhao-Hui], Du, P.J.[Pei-Jun], Li, J.[Jun], Su, H.J.[Hong-Jun],
Simultaneous Sparse Graph Embedding for Hyperspectral Image Classification,
GeoRS(53), No. 11, November 2015, pp. 6114-6133.
IEEE DOI 1509
feature extraction BibRef

Xue, Z.H.[Zhao-Hui], Du, P.J.[Pei-Jun], Li, J.[Jun], Su, H.J.[Hong-Jun],
Sparse Graph Regularization for Hyperspectral Remote Sensing Image Classification,
GeoRS(55), No. 4, April 2017, pp. 2351-2366.
IEEE DOI 1704
geophysical image processing BibRef

Maronidis, A., Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Subclass Graph Embedding and a Marginal Fisher Analysis paradigm,
PR(48), No. 12, 2015, pp. 4024-4035.
Elsevier DOI 1509
Dimensionality reduction BibRef

Iosifidis, A.[Alexandros], Tefas, A.[Anastasios], Pitas, I.[Ioannis],
Sparse extreme learning machine classifier exploiting intrinsic graphs,
PRL(65), No. 1, 2015, pp. 192-196.
Elsevier DOI 1511
Sparse extreme learning machine BibRef

Vretos, N., Tefas, A., Pitas, I.,
A novel dimensionality reduction technique based on kernel optimization through graph embedding,
SIViP(9), No. 1 Supp, December 2015, pp. 3-10.
WWW Link. 1601
BibRef

Huang, S.[Sheng], Yu, Y.[Yang], Yang, D.[Dan], Elgammal, A.M.[Ahmed M.], Yang, D.[Dong],
Collaborative Graph Embedding: A Simple Way to Generally Enhance Subspace Learning Algorithms,
CirSysVideo(26), No. 10, October 2016, pp. 1835-1845.
IEEE DOI 1610
Algorithm design and analysis BibRef

Huang, S.[Sheng], Elgammal, A.E.[Ahmed E.], Yang, D.[Dan],
On the effect of hyperedge weights on hypergraph learning,
IVC(57), No. 1, 2017, pp. 89-101.
Elsevier DOI 1702
Hypergraph learning BibRef

Jian, M.[Meng], Jung, C.[Cheolkon], Zheng, Y.G.[Yao-Guo],
Discriminative Structure Learning for Semantic Concept Detection With Graph Embedding,
MultMed(16), No. 2, February 2014, pp. 413-426.
IEEE DOI 1404
content management BibRef

Jian, M.[Meng], Jung, C.[Cheolkon],
Semi-Supervised Bi-Dictionary Learning for Image Classification With Smooth Representation-Based Label Propagation,
MultMed(18), No. 3, March 2016, pp. 458-473.
IEEE DOI 1603
Bridges BibRef

Vento, M.[Mario],
A long trip in the charming world of graphs for Pattern Recognition,
PR(48), No. 2, 2015, pp. 291-301.
Elsevier DOI 1411
Graph clustering BibRef

Foggia, P.[Pasquale], Vento, M.[Mario],
Graph Embedding for Pattern Recognition,
ICPR-Contests10(75-82).
Springer DOI 1008
BibRef

Chen, Y.L.[Yi-Lei], Hsu, C.T.[Chiou-Ting],
Multilinear Graph Embedding: Representation and Regularization for Images,
IP(23), No. 2, February 2014, pp. 741-754.
IEEE DOI 1402
graph theory BibRef

Mousavi, S.F.[Seyedeh Fatemeh], Safayani, M.[Mehran], Mirzaei, A.[Abdolreza], Bahonar, H.[Hoda],
Hierarchical graph embedding in vector space by graph pyramid,
PR(61), No. 1, 2017, pp. 245-254.
Elsevier DOI 1705
Graph embedding BibRef

Sun, Y.[Yubao], Wang, S.J.[Su-Juan], Liu, Q.S.[Qing-Shan], Hang, R.L.[Ren-Long], Liu, G.C.[Guang-Can],
Hypergraph Embedding for Spatial-Spectral Joint Feature Extraction in Hyperspectral Images,
RS(9), No. 5, 2017, pp. xx-yy.
DOI Link 1706
BibRef

Wang, X.D.[Xiao-Dong], Chen, R.C.[Rung-Ching], Hong, C.Q.[Chao-Qun], Zeng, Z.Q.[Zhi-Qiang], Zhou, Z.L.[Zhi-Li],
Semi-supervised multi-label feature selection via label correlation analysis with L1-norm graph embedding,
IVC(63), No. 1, 2017, pp. 10-23.
Elsevier DOI 1706
Semi-supervised, learning BibRef

Jansen, A.[Aren], Sell, G.[Gregory], Lyzinski, V.[Vince],
Scalable out-of-sample extension of graph embeddings using deep neural networks,
PRL(94), No. 1, 2017, pp. 1-6.
Elsevier DOI 1708
Deep neural networks BibRef

Bahonar, H.[Hoda], Mirzaei, A.[Abdolreza], Wilson, R.C.[Richard C.],
Diffusion wavelet embedding: A multi-resolution approach for graph embedding in vector space,
PR(74), No. 1, 2018, pp. 518-530.
Elsevier DOI 1711
Spectral graph embedding. BibRef

Wang, Y., Zhang, L., Tong, X., Nie, F., Huang, H., Mei, J.,
LRAGE: Learning Latent Relationships With Adaptive Graph Embedding for Aerial Scene Classification,
GeoRS(56), No. 2, February 2018, pp. 621-634.
IEEE DOI 1802
Feature extraction, Kernel, Learning systems, Linear programming, Measurement, Principal component analysis, Satellites, latent relationship learning BibRef

Yuan, Y.H.[Yun-Hao], Sun, Q.S.[Quan-Sen],
Graph regularized multiset canonical correlations with applications to joint feature extraction,
PR(47), No. 12, 2014, pp. 3907-3919.
Elsevier DOI 1410
Pattern recognition BibRef

Yuan, Y.H.[Yun-Hao], Sun, Q.S.[Quan-Sen], Ge, H.W.[Hong-Wei],
Fractional-order embedding canonical correlation analysis and its applications to multi-view dimensionality reduction and recognition,
PR(47), No. 3, 2014, pp. 1411-1424.
Elsevier DOI 1312
Pattern recognition BibRef

Su, S.Z.[Shu-Zhi], Ge, H.W.[Hong-Wei], Yuan, Y.H.[Yun-Hao],
Multi-patch embedding canonical correlation analysis for multi-view feature learning,
JVCIR(41), No. 1, 2016, pp. 47-57.
Elsevier DOI 1612
Multi-view feature learning BibRef

Su, S.Z.[Shu-Zhi], Ge, H.W.[Hong-Wei], Tong, Y.B.[Yu-Bing],
Multi-graph embedding discriminative correlation feature learning for image recognition,
SP:IC(60), No. 1, 2018, pp. 173-182.
Elsevier DOI 1712
Image recognition BibRef

Wang, S., Zhu, W.,
Sparse Graph Embedding Unsupervised Feature Selection,
SMCS(48), No. 3, March 2018, pp. 329-341.
IEEE DOI 1802
Algorithm design and analysis, Clustering algorithms, Dictionaries, Encoding, Machine learning algorithms, Optimization, unsupervised learning BibRef

Cui, P.[Peng], Liu, S.W.[Shao-Wei], Zhu, W.W.[Wen-Wu],
General Knowledge Embedded Image Representation Learning,
MultMed(20), No. 1, January 2018, pp. 198-207.
IEEE DOI 1801
common-sense reasoning, convolution, graph theory, image representation, knowledge based systems, multirelational graph embedding BibRef

Liu, T.[Tianchi], Lekamalage, C.K.L.[Chamara Kasun Liyanaarachchi], Huang, G.B.[Guang-Bin], Lin, Z.P.[Zhi-Ping],
An adaptive graph learning method based on dual data representations for clustering,
PR(77), 2018, pp. 126-139.
Elsevier DOI 1802
Graph-based clustering, Constrained Laplacian rank, Extreme learning machine, Embedding, Graph Laplacian BibRef

Abeo, T.A.[Timothy Apasiba], Shen, X.J.[Xiang-Jun], Gou, J.P.[Jian-Ping], Mao, Q.R.[Qi-Rong], Bao, B.K.[Bing-Kun], Li, S.Y.[Shu-Ying],
Dictionary-induced least squares framework for multi-view dimensionality reduction with multi-manifold embeddings,
IET-CV(13), No. 2, March 2019, pp. 97-108.
DOI Link BibRef 1903

Abeo, T.A.[Timothy Apasiba], Shen, X.J.[Xiang-Jun], Bao, B.K.[Bing-Kun], Zha, Z.J.[Zheng-Jun], Fan, J.P.[Jian-Ping],
A generalized multi-dictionary least squares framework regularized with multi-graph embeddings,
PR(90), 2019, pp. 1-11.
Elsevier DOI 1903
Multi-view dimension reduction, Least squares, Multiple graphs, Feature extraction, Classification BibRef

Wang, S.S.[Shang-Si], Arroyo, J.[Jesús], Vogelstein, J.T.[Joshua T.], Priebe, C.E.[Carey E.],
Joint Embedding of Graphs,
PAMI(43), No. 4, April 2021, pp. 1324-1336.
IEEE DOI 2103
Feature extraction, Symmetric matrices, Numerical models, Task analysis, Inference algorithms, Stochastic processes, statistical inference BibRef

Shen, C.C.[Cen-Cheng], Wang, S.S.[Shang-Si], Badea, A.[Alexandra], Priebe, C.E.[Carey E.], Vogelstein, J.T.[Joshua T.],
Discovering the signal subgraph: An iterative screening approach on graphs,
PRL(184), 2024, pp. 97-102.
Elsevier DOI 2408
Iterative screening, Distance correlation, Graph classification BibRef

França, G.[Guilherme], Rizzo, M.L.[Maria L.], Vogelstein, J.T.[Joshua T.],
Kernel k-Groups via Hartigan's Method,
PAMI(43), No. 12, December 2021, pp. 4411-4425.
IEEE DOI 2112
Energy efficiency, Hilbert space, Probability distribution, Extraterrestrial measurements, Machine learning, stochastic block model BibRef

Lu, J.L.[Jiang-Lin], Wang, H.L.[Hai-Ling], Zhou, J.[Jie], Chen, Y.D.[Yu-Dong], Lai, Z.H.[Zhi-Hui], Hu, Q.H.[Qing-Hua],
Low-rank adaptive graph embedding for unsupervised feature extraction,
PR(113), 2021, pp. 107758.
Elsevier DOI 2103
Low-rank regression, Jointly sparse learning, Adaptive graph embedding, Unsupervised feature extraction BibRef

Zhang, B.[Bin], Qiang, Q.Y.[Qian-Yao], Wang, F.[Fei], Nie, F.P.[Fei-Ping],
Flexible Multi-View Unsupervised Graph Embedding,
IP(30), 2021, pp. 4143-4156.
IEEE DOI 2104
Dimensionality reduction, Task analysis, Principal component analysis, Sparse matrices, Laplace equations, graph embedding BibRef

Yang, H.[Hong], Chen, L.[Ling], Pan, S.R.[Shi-Rui], Wang, H.S.[Hai-Shuai], Zhang, P.[Peng],
Discrete embedding for attributed graphs,
PR(123), 2022, pp. 108368.
Elsevier DOI 2112
Attributed graphs, Graph embedding, Weisfeiler-Lehman graph kernels, Learning to hash, Low-bit quantization BibRef

Guo, L.[Lin], Dai, Q.[Qun],
Graph Clustering via Variational Graph Embedding,
PR(122), 2022, pp. 108334.
Elsevier DOI 2112
Graph convolution neural network, Variational graph embedding, Graph clustering, Variational graph auto-encoder BibRef

Wan, M.H.[Ming-Hua], Chen, X.Y.[Xue-Yu], Zhan, T.M.[Tian-Ming], Yang, G.[Guowei], Tan, H.[Hai], Zheng, H.[Hao],
Low-rank 2D local discriminant graph embedding for robust image feature extraction,
PR(133), 2023, pp. 109034.
Elsevier DOI 2210
Feature extraction, Two-dimensional locality preserving projections (2DLPP), Discrimination information BibRef

Agibetov, A.[Asan],
Neural graph embeddings as explicit low-rank matrix factorization for link prediction,
PR(133), 2023, pp. 108977.
Elsevier DOI 2210
Graph embedding, Random walks, Matrix factorization, Information theory, Link prediction BibRef

Hu, L.C.[Liang-Chen], Dai, Z.L.[Zhen-Lei], Tian, L.[Lei], Zhang, W.S.[Wen-Sheng],
Class-Oriented Self-Learning Graph Embedding for Image Compact Representation,
CirSysVideo(33), No. 1, January 2023, pp. 74-87.
IEEE DOI 2301
Sparse matrices, Manifolds, Machine learning algorithms, Laplace equations, Heuristic algorithms, Data models, Data mining, compact representation BibRef

Yuan, R.W.[Rui-Wen], Tang, Y.Q.[Yong-Qiang], Xiao, Y.H.[Yang-Hao], Zhang, W.S.[Wen-Sheng],
IBCS: Learning Information Bottleneck-Constrained Denoised Causal Subgraph for Graph Classification,
PAMI(47), No. 3, March 2025, pp. 1627-1643.
IEEE DOI 2502
Noise measurement, Noise, Training, Feature extraction, Data mining, Correlation, Automation, Vectors, Semantics, Object recognition, information bottleneck BibRef

Riba, P.[Pau], Lladós, J.[Josep], Fornés, A.[Alicia], Dutta, A.[Anjan],
Large-Scale Graph Indexing Using Binary Embeddings of Node Contexts,
GbRPR15(208-217).
Springer DOI 1511
BibRef

Naeemi, M.A., Mohseni, H.,
Evaluation of graph embedding approach for dimensionality reduction using different kernels,
IPRIA17(69-74)
IEEE DOI 1712
data analysis, geometry, graph theory, image classification, linearisation techniques, statistical analysis, similarity graph BibRef

Robles-Kelly, A., Wei, R.[Ran],
Semi-supervised image labelling using barycentric graph embeddings,
ICPR16(1518-1523)
IEEE DOI 1705
Cost function, Eigenvalues and eigenfunctions, Image color analysis, Image edge detection, Labeling, Laplace equations, Mathematical, model BibRef

Fukui, K., Okuno, A., Shimodaira, H.,
Image and tag retrieval by leveraging image-group links with multi-domain graph embedding,
ICIP16(221-225)
IEEE DOI 1610
Correlation BibRef

Jiménez-Guarneros, M.[Magdiel], Carrasco-Ochoa, J.A.[Jesús Ariel], Martínez-Trinidad, J.F.[José Francisco],
Prototype Selection for Graph Embedding Using Instance Selection,
MCPR15(84-92).
Springer DOI 1506

See also Empirical Study of Oversampling and Undersampling for Instance Selection Methods on Imbalance Datasets, An. BibRef

Aydos, F.[Fahri], Soran, A.[Ahmet], Demirci, M.F.[M. Fatih],
Class Representative Computation Using Graph Embedding,
CIAP13(I:452-461).
Springer DOI 1311
BibRef

Huang, Z.W.[Zhi-Wu], Shan, S.G.[Shi-Guang], Zhang, H.H.[Hai-Hong], Lao, S.H.[Shi-Hong], Chen, X.L.[Xi-Lin],
Cross-view Graph Embedding,
ACCV12(II:770-781).
Springer DOI 1304
face recognition across poses and face recognition across resolutions. BibRef

Olvera-López, J.A.[J. Arturo], Carrasco-Ochoa, J.A.[J. Ariel], Martínez-Trinidad, J.F.[José Francisco],
Prototype Selection Via Prototype Relevance,
CIARP08(153-160).
Springer DOI 0809
BibRef

Yang, J.C.[Jian-Chao], Yang, S.C.[Shui-Cheng], Fu, Y.[Yun], Li, X.L.[Xue-Long], Huang, T.S.[Thomas S.],
Non-negative graph embedding,
CVPR08(1-8).
IEEE DOI 0806
BibRef

Chapter on Matching and Recognition Using Volumes, High Level Vision Techniques, Invariants continues in
Linear Prediction Techniques .